Fragmentation analysis of blasted rock is an important process control activity, particularly in underground mining, for optimizing productivity and cost. Fragmentation analysis includes the estimation of mean particle size and the size distribution related to percent passing in a sieve analysis. With the advancement of camera and imaging technology, the use of photographic based image analysis systems has become a convenient and better alternative to traditional sieving of rock blasts. As a result, mining industries have recently taken an initiative to implement automated image analysis systems for rock fragmentation analysis.
An automated image analysis system has the capacity to monitor the blasted material quality continuously as opposed to only sampling data analysis as in the case of traditional sieving. However, automated image analysis processing requires the images to be taken under controlled lighting conditions to produce more consistent results. Further, commercially available fragmentation software packages are only able to produce fragmentation distribution curves from single digital, print photographs, 35-mm slides or videotapes. For each image, a scale needs to be selected and defined to create a net. Established algorithms are then used on the net to generate a cumulative fragmentation distribution curve. A major drawback of these available software packages is that they can only analyze one image at a time. Determination of fragmentation, however, generally requires analysis of many images.
Accordingly, there is a need for a rock fragmentation system capable of processing multiple digital photographs of fragment material, e.g., rock piles, within a file directory, generating a fragmentation distribution for each digital photograph, and a total fragmentation distribution based on all the digital photographs.